Results 291 to 300 of about 201,128 (321)

Phonotactics in Inductive Logic Programming

2004
We examine the results of applying inductive logic programming (ILP) to a relatively simple linguistic task, that of recognizing monosyllables in one language. ILP is suited to linguistic problems given linguists' preference for formulating their theories in discrete rules, and because of ILP's ability to incorporate various background theories. But it
John Nerbonne, Stasinos Konstantopoulos
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QuickFOIL: Scalable Inductive Logic Programming

Proceedings of the VLDB Endowment, 2014
Inductive Logic Programming (ILP) is a classic machine learning technique that learns first-order rules from relational-structured data. However, to-date most ILP systems can only be applied to small datasets (tens of thousands of examples).
Qiang Zeng, J. Patel, David Page
semanticscholar   +1 more source

Inductive logic programming

New Generation Computing, 1991
A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears.
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Bayesian Inductive Logic Programming

Proceedings of the seventh annual conference on Computational learning theory - COLT '94, 1994
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learning and Computational Learning Theory, ILP is based on lock-step development of Theory, Implementations and Applications.
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Efficient program synthesis using constraint satisfaction in inductive logic programming

Journal of machine learning research, 2013
We present NrSample, a framework for program synthesis in inductive logic programming. NrSample uses propositional logic constraints to exclude undesirable candidates from the search. This is achieved by representing constraints as propositional formulae
John Ahlgren, S. Y. Yuen
semanticscholar   +1 more source

Support Vector Inductive Logic Programming [PDF]

, 2005
In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP). We propose a general method for constructing kernels for Support Vector Inductive Logic Programming (SVILP).
Huma Lodhi, Stephen Muggleton, Ata Amini, Michael J.E. Sternberg   +3 more
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Grammar Induction as Substructural Inductive Logic Programming

2000
In this chapter we describe an approach to grammar induction based on categorial grammars: the EMILE algorithm. Categorial grammars are equivalent to context-free grammars. They were introduced by Ajduciewicz and formalised by Lambek. Technically they can be seen as a variant of the propositional calculus without structural rules.
de Haas, E., Adriaans, P.W.
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Applications of inductive logic programming

Communications of the ACM, 1995
Techniques of machine learning have been successfully applied to various problems [1, 12]. Most of these applications rely on attribute-based learning, exemplified by the induction of decision trees as in the program C4.5 [20]. Broadly speaking, attribute-based learning also includes such approaches to learning as neural networks and nearest neighbor ...
Stephen Muggleton, Ivan Bratko
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Inductive logic programming and learnability

ACM SIGART Bulletin, 1994
The paper gives an overview of theoretical results in the rapidly growing field of inductive logic programming (ILP). The ILP learning situation (generality model, background knowledge, examples, hypotheses) is formally characterized and various restrictions of it are discussed in the light of their impact on learnability.
Jörg-Uwe Kietz, Sašo Džeroski
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Cautious induction in inductive logic programming

1997
Many top-down Inductive Logic Programming systems use a greedy, covering approach to construct hypotheses. This paper presents an alternative, cautious approach, known as cautious induction. We conjecture that cautious induction can allow better hypotheses to be found, with respect to some hypothesis quality criteria.
Simon Anthony, Alan M. Frisch
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